Combining predictive distributions for statistical post-processing of ensemble forecasts

TL;DR

This paper explores combining multiple EMOS models with different parametric distributions to improve the accuracy of ensemble weather forecasts, demonstrating potential benefits through case studies.

Contribution

It introduces and evaluates methods for combining predictive distributions from various EMOS models, advancing ensemble post-processing techniques.

Findings

Combining EMOS models can enhance forecast accuracy.

Forecast combination methods outperform single-model approaches.

Case studies show improved skill in wind speed and precipitation predictions.

Abstract

Statistical post-processing techniques are now widely used to correct systematic biases and errors in calibration of ensemble forecasts obtained from multiple runs of numerical weather prediction models. A standard approach is the ensemble model output statistics (EMOS) method, a distributional regression approach where the forecast distribution is given by a single parametric law with parameters depending on the ensemble members. Choosing an appropriate parametric family for the weather variable of interest is a critical, however, often non-trivial task, and has been the focus of much recent research. In this article, we assess the merits of combining predictive distributions from multiple EMOS models based on different parametric families. In four case studies with wind speed and precipitation forecasts from two ensemble prediction systems, we study whether state of the art forecast combination methods are able to improve forecast skill.